论文标题
预测性和中风成功 - 网球比赛中计算机辅助的球员咕unt分析
Predicting Sex and Stroke Success -- Computer-aided Player Grunt Analysis in Tennis Matches
论文作者
论文摘要
专业运动员越来越多地使用对元数据和信号数据的自动分析来改善其训练和游戏性能。与其他相关的人类对人类研究领域一样，特别是信号数据，包含重要的性能和特定于情绪的指标，以进行自动分析。在本文中，我们介绍了新颖的数据集分数！为了调查几个功能和机器学习范例的表现，以预测性别和立即中风在网球比赛中的成功，仅基于玩家的咕unt声表达。数据是从YouTube收集的，标记在完全相同的定义下，并处理了用于建模的音频。我们提取了音频样本的几种广泛使用的基本，专家知识和深层声学特征，并与各种机器学习方法相结合评估了它们的有效性。在二进制环境中，使用频谱图和卷积复发性神经网络的最佳系统在播放器性别预测任务中实现了未加权的平均召回率（UAR）为84.0％，而60.3％的人仅基于两种性别狂热的人的声音提示，预测中风成功。此外，当模型分别以女性或男性咕unt术进行训练时，我们达到了58.3％和61.3％的UAR。
Professional athletes increasingly use automated analysis of meta- and signal data to improve their training and game performance. As in other related human-to-human research fields, signal data, in particular, contain important performance- and mood-specific indicators for automated analysis. In this paper, we introduce the novel data set SCORE! to investigate the performance of several features and machine learning paradigms in the prediction of the sex and immediate stroke success in tennis matches, based only on vocal expression through players' grunts. The data was gathered from YouTube, labelled under the exact same definition, and the audio processed for modelling. We extract several widely used basic, expert-knowledge, and deep acoustic features of the audio samples and evaluate their effectiveness in combination with various machine learning approaches. In a binary setting, the best system, using spectrograms and a Convolutional Recurrent Neural Network, achieves an unweighted average recall (UAR) of 84.0 % for the player sex prediction task, and 60.3 % predicting stroke success, based only on acoustic cues in players' grunts of both sexes. Further, we achieve a UAR of 58.3 %, and 61.3 %, when the models are exclusively trained on female or male grunts, respectively.
